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All Journal Management Studies and Entrepreneurship Journal (MSEJ) Journal of Management and Bussines (JOMB) Journal of Computer Networks, Architecture and High Performance Computing
Felix Felix
Universitas Prima Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education

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Perilaku Kepemimpinan, Promosi Jabatan, dan Lingkungan Kerja Fisik terhadap Kinerja Karyawan Cyntia Cokro; Cindy Tifani; Bagus Bagus; Jocelyn Tania; Felix Felix
Journal of Management and Bussines (JOMB) Vol 4 No 2 (2022): Journal of Management and Bussines (JOMB)
Publisher : IPM2KPE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/jomb.v4i2.4453

Abstract

This research aims to examine and analyze the influence of leadership behavior, promotion, and physical work environment on the performance of employees of PT. Pawnshop (Persero) Medan. This research method is descriptive quantitative. Population and research sample using saturated sampling technique. Data analysis techniques start from data compilation, data tabulation to the testing stage. The statistical method used is multiple linear regression analysis. The results showed that 89.3% of employee performance can be explained by independent variables and the remaining 10.7% is explained by other factors that have not been considered in this regression. In conclusion, simultaneously and partially leadership behavior, promotion, and physical work environment have a significant effect on the performance of employees of PT. Pawnshop (Persero) Medan. Keywords:Employee Performance, Physical Work Environment, Leadership Behavior, Promotion
Pengaruh Kebijakan Dividen, Rasio Likuiditas, Net Profit Margin, Rasio Solvabilitas, Earning Per Share, Firm Size Dan Rasio Aktivitas Terhadap Return Saham Pada Perusahaan Sektor Perdagangan, Jasa Dan Investasi Yang Tercatat Di Bursa Efek Indonesia Annisa Nauli Sinaga; Felix Felix; Anastasya Anastasya; Vanny Luardy; Mitha Christina Ginting
Management Studies and Entrepreneurship Journal (MSEJ) Vol. 4 No. 4 (2023): Management Studies and Entrepreneurship Journal (MSEJ)
Publisher : Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/msej.v4i4.2419

Abstract

Penelitian ini untuk mengetahui Pengaruh Kebijakan Dividen, Rasio Likuiditas, Net Profit Margin, Rasio Solvabilitas, Earning Per Share, Firm Size dan Rasio Aktivitas terhadap Return Saham. Populasi adalah perusahaan sektor perdagangan, jasa dan investasi periode 2019-2021 dan sampel diperoleh 17 perusahaan. Analisis regresi linear berganda digunakan sebagai strategi dalam metode statistik. Hasil penelitian menunjukkan bahwa hanya Firm Size yang berpengaruh terhadap Return Saham secara parsial, sedangkan Kebijakan Dividen, Rasio Likuiditas, Net Profit Margin, Rasio Solvabilitas, Earning Per Share dan Rasio Aktivitas tidak berpengaruh terhadap Return Saham. Dan juga secara simultan menyatakan Kebijakan Dividen, Rasio Likuiditas, Net Profit Margin, Rasio Solvabilitas, Earning Per Share, Firm Size dan Rasio Aktivitas tidak berpengaruh terhadap Return Saham.
Analysis of the Support Vector Machine Algorithm for Predicting New Student Admissions at MA Taruna AL Jabbar Faustina Wardhana; Felix Felix; Elvis Ompusunggu
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8799

Abstract

Madrasah Aliyah (MA) Taruna Teknik Al Jabar is an educational institution sponsored by the Ministry of Education and Culture that holds a new student admission program periodically every year. However, the number of students accepted tends to increase or decrease every year. This situation needs to be analyzed carefully because it affects the school's policy in achieving the school's educational goals both qualitatively and quantitatively. This problem requires systematic planning and design to develop effective solutions to improve the quality of new student admissions. The algorithm applied is Support Vector Machine which is a method commonly used for classification and regression. In classification modeling, SVM has a more mature and mathematically clear concept than other classification methods. The results achieved 100% prediction accuracy from 20 data samples, and the results of the prediction data test showed that students with registration number 25023 were declared passed, while students with registration number 25024 were declared failed.
Co-Authors Anastasya Anastasya Annisa Nauli Sinaga Bagus Bagus Cindy Tifani Cyntia Cokro Elvis Ompusunggu Faustina Wardhana Jocelyn Tania Mitha Christina Ginting Vanny Luardy